Module rust_bert::roberta

Expand description

RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al.)

Implementation of the RoBERTa language model (https://arxiv.org/abs/1907.11692 Liu, Ott, Goyal, Du, Joshi, Chen, Levy, Lewis, Zettlemoyer, Stoyanov, 2019). The base model is implemented in the bert_model::BertModel struct. Several language model heads have also been implemented, including:

Masked language model: roberta_model::RobertaForMaskedLM
Multiple choices: roberta_model:RobertaForMultipleChoice
Question answering: roberta_model::RobertaForQuestionAnswering
Sequence classification: roberta_model::RobertaForSequenceClassification
Token classification (e.g. NER, POS tagging): roberta_model::RobertaForTokenClassification

Model set-up and pre-trained weights loading

The example below illustrate a Masked language model example, the structure is similar for other models. All models expect the following resources:

Configuration file expected to have a structure following the Transformers library
Model weights are expected to have a structure and parameter names following the Transformers library. A conversion using the Python utility scripts is required to convert the .bin weights to the .ot format.
RobertaTokenizer using a vocab.txt vocabulary and merges.txt 2-gram merges Pretrained models are available and can be downloaded using RemoteResources.

use tch::{nn, Device};
use rust_bert::bert::BertConfig;
use rust_bert::resources::{LocalResource, ResourceProvider};
use rust_bert::roberta::RobertaForMaskedLM;
use rust_bert::Config;
use rust_tokenizers::tokenizer::RobertaTokenizer;

let config_resource = LocalResource {
    local_path: PathBuf::from("path/to/config.json"),
};
let vocab_resource = LocalResource {
    local_path: PathBuf::from("path/to/vocab.txt"),
};
let merges_resource = LocalResource {
    local_path: PathBuf::from("path/to/merges.txt"),
};
let weights_resource = LocalResource {
    local_path: PathBuf::from("path/to/model.ot"),
};
let config_path = config_resource.get_local_path()?;
let vocab_path = vocab_resource.get_local_path()?;
let merges_path = merges_resource.get_local_path()?;
let weights_path = weights_resource.get_local_path()?;

let device = Device::cuda_if_available();
let mut vs = nn::VarStore::new(device);
let tokenizer: RobertaTokenizer = RobertaTokenizer::from_file(
    vocab_path.to_str().unwrap(),
    merges_path.to_str().unwrap(),
    true,
    true,
)?;
let config = BertConfig::from_file(config_path);
let bert_model = RobertaForMaskedLM::new(&vs.root(), &config);
vs.load(weights_path)?;